Médecine de précision en oncologie

Transcription

1 Médecine de précision en oncologie Académie Nationale de Pharmacie Mercredi 3 octobre 2018 Antoine Hollebecque, MD Département d Innovation Thérapeutique et Essais précoces (DITEP) Gustave Roussy

2 Precision medicine: definition from ESMO Yates, Ann Oncol, 2018

3 Which rationale for this concept? Genomic characterization of cancers have shown that frequent cancers include large number of genomic segments

4 Impact of targeting mechanisms of cancer progression Estimated survival of metastatic cancers according to molecular alteration (months) KIT B-RAF AR expression ALK EGFR ERBB2 amp ER expression average VHL good prono VHL pro inter VHL mut poor outcome Identification and targeting molecular mechanisms involved in cancer progression improves outcome Similar concept applies with immunotherapeutics (PDL1)

5 Maximum Percent Reduction from Baseline Measurement Increase efficacy Best Confirmed Response ~2% NSCLC B-RAF inhibitor in NSCLC (V600E BRAF mutation) ESMO 2014 P S PR D N E * * Nonsmoker Smoker, 40 pack years Smoker, > 40 pack years EGFR inhibitor * in NSCLC (EGFR mutation) Lancet Oncol 2012 * * * * ALK inhibitor in NSCLC (ALK rearrangment) NEJM 2010 ~4% NSCLC ROS1 inhibitor in NSCLC (ROS1 rearrangment) NEJM 2014 ~10% NSCLC ~1% NSCLC Courtesy of B. Besse

6 Speed-up drug development Crizotinib 4 years

7 Gustave Roussy molecular screening program MOSCATO Patients with lethal Cancer Tumor Specimen Molecular profiling Targeted therapy according to the molecular profile Intelling reports Can molecular profiling improve patient outcome?

8 Design of MOSCATO High through-put analysis in a high volume phase I center Monocentric Target accrual => 1000 patients BIOPSY FRESH TUMOR PATHOLOGICAL CONTROL MOLECULAR SCREENING CGH Array & NGS & WES & RNAseq CLINICAL DECISION TREATMENT Max 21 calendar days

9 Objectives of MOSCATO Primary Objective: To show that broad molecular screening improves outcome Stastistical hypothesis: > 25% of patients treated according to their genomic alteration will experience a clinical benefit defined by a PFS ratio > 1.3 PFS 1 PFS 2 Standard Therapy Relevant Molecular Targeted Agent (MOSCATO) PFS 2 PFS1 > 1.3 Tumor Progression Tumor Progression Secondary Objectives To assess the feasability of this approach To improve tumor response To assess the percentage of patients treated with a selected therapy To assess the frequency of genomic alterations To speed-up drug development through enrichment of trials in biomarker-defined patients (stratified medicine)

10 Methods of MOSCATO Analysis Improvement: 30 genes then 50 then 75 genes screened then moved partially in may 2014 to WES + RNASeq BIOPSY FRESH TUMOR PATHOLOGICAL CONTROL MOLECULAR SCREENING CGH & NGS & RNAseq & WES CLINICAL DECISION TREATMENT CGH array Agilent (180K, Whole genome coverage) Ion Torrent PGM Life Technologies (Ampliseq CHP2 + custom n=75 genes, Dec 2013) FGFR1 amplification + DNA extrac on ng Mul plex PCR 1450 amplicons IonTorrent/PGM (> 500X coverage) Torrent_Suite v2.2 e Confimd by Sanger Sequencing Standardized Report

11 Methods of MOSCATO BIOPSY FRESH TUMOR PATHOLOGICAL CONTROL MOLECULAR SCREENING CGH & NGS & RNAseq & WES CLINICAL DECISON TREATMENT Clinical Decision Weekly Tumor Board discussion Clinicians / Biologists / Bioinformaticians Treatment Phase I/II trial compounds > 60 phase I Gustave Roussy > 20 Actionable Targets Off label use of EMA approved MTA Hanahan D, Weinberg R.A. Cell 2011.

12 RESULTS of MOSCATO 1000 adults

13 Biopsy and decision in MOSCATO 01 are performed in clinically relevant timing Median 24 days IC days Median 25 days IC days

14 Summary of MOSCATO results Patients included n= 1110 Adults included n= 1036 Molecular tumour board (MTB) n=949 Pediatrics N=74 Screen failure N=87 No possible biopsy n=28 SAE: n=25 Consent withdrawal: n=11 Clinical deterioration or death: n=5 Other: 11 Missing: n=7 19% 49 % Molecular portrait (NGS or CGH) n=844 NGS+CGH: n=740 / NGS alone: n=98 / CGH alone: n=6 Actionable Target, n= 411 (IHC alone n=1) Received Matched Treatment n=199 No NGS nor CGH: n=103 No Actionable Target based on NGS or CGH: n=434 rapid clinical deterioration: n=62 waiting for treatment: n=37 exclusion criteria n=18 trial not open n=12 absence of progressive disease n=6 patient refusal n=3 Other n=64, unknown n=6: Primary endpoint completed n= 193 PFS1 missing: n=5 PFS2<1.3*PFS1 and not yet progressed n=1 Massard et al. Cancer Discov 2018

15 Results for primary endpoint Tumor Progression Tumor Progression Previous Therapy Molecular Targeted Agent (MOSCATO) Results PFS ratio > 1.3 = 33% IC95 PFS ratio = 26%-39% H0 rejected with a p-value < Massard et al. Cancer Discov 2018

16 Results for secondary endpoints CR + PR = 11% (22/193) CR + PR + SD = 63% (122/193) Best percent change from baseline (RECIST1.1) + 20% - 30% Massard et al. Cancer Discov 2018

17 Winner tumor type e.g. cholangiocarcinoma Rebiopsy procedures = 1 patient x 3 3 patients x 2 MOSCATO-01 N=1168 Patients N=43 (4%) Biopsies N=48 Fit for analysis N=38 (79%) No biopsy = 3 Cellularity < 10% = 6 Not realized = 1 No alteration found N=5 50% Druggable molecular aberration(s) N=25 (66%) Treated N=19 (76%) Verlingue et al. Eur J Cancer 2017

18 Winner tumor type e.g. cholangiocarcinoma Median PFS ratio = 1.52 IC95 [0,08-7,1] PFS ratio > 1.3 = 44% Response rates Progression free survival PR+CR = 32% 6-month PFS rate = 42%

19 Winner Pathway e.g. ERBB3 ERBB3 alterations N=33 (3.7%) 1 patient with 2 alterations in ERBB3 Amp N=2 Hotspots N=6 VUP N=13 NPV N=13 Received therapies N=28 NPV : non pathogenic variant VUP : variant of unknown pathogenicity Amp: amplification HER3 partners inhibitors N=11 Other treatments N=17

20 Winner Pathway e.g. ERBB3 Overall survival HER3 partners inhibitors: - Trastuzumab and/or lapatinib - Afatinib

21 ERBB3 V140L A159T I III IV II K329E Extracellular domain G661S Transmembrane domain Ongoing treatment E928G S846I Q865H Kinase domain PFS < 100 days PFS > 200 days Docking domain HER2 directed monoclonal antibodies HER2/pan HER directed small molecules

22 Current questions? Should we use large panel of genes? How to develop drugs in rare genomic segments? Prediction of sensitivity? Detecting subclones and monitoring clonal evolution?

23 Current questions? Should we use large panel of genes? How to develop drugs in rare genomic segments? Prediction of sensitivity? Detecting subclones and monitoring clonal evolution?

24 Should we use large panel of genes? Because it allows testing the few relevant genes in a single assay Because it allows finding alterations in other genes for which relevance to cancer is shown but there is no drug approval Because it allows having «global picture» of cancer genome and mutational processes Because it allows modeling disease biology in each patient

25 Should we use large panel of genes? EGFR ALK RET BRAF MET ERBB2 ROS1 others FDA approved Thermo Fisher Scientific s Oncomine Dx Target Test as the first next-generation sequencing (NGS)-based companion diagnostic that screens tumor samples against panels of biomarkers

26 Should we use large panel of genes? Femme 30 ans Cholangiocarcinome intra-hépatique (Avril 2011) En progression après: Mai 2011: Radioembolisation (Yttrium90) Mai-Sep 2011: Folfox Fev-Juin 2012: Folfox Juin-Dec 2012: Gemcitabine-Cisplatine Jan-Avr 2013: Sunitinib Avr-Mai 2013: Paclitaxel Juin 2013: MOSCATO WES et RNAseq tngs No mutation Translocation FGFR2-CCAR1

27 Should we use large panel of genes? 26/11/ /02/2015 Fusion FGFR2-CCAR1 FGFR inhibitor

28 Current questions? Should we use large panel of genes? How to develop drugs in rare genomic segments? Prediction of sensitivity? Detecting subclones and monitoring clonal evolution?

29 Drug development in rare genomic entities

30 Drug development in rare genomic entities

31 Drug development in rare genomic entities Larotrectinib

32 Drug development in rare genomic entities Andre F, NEJM, 2018

33 Drug development in rare genomic entities Basket protocol 1 molécule 1 ou plusieurs anomalies moléculaires plusieurs types tumoraux

34 Drug development in rare genomic entities Efficacy according to histology? e.g. BRAF Hyman DM et al. NEJM 2015 Hyman DM et al. NEJM 2015

35 Drug development in rare genomic entities Mutation BRAF V600E Mélanome Mutation BRAF V600E Cancer du colon ORR 50% ORR = 5% Chapman et al. N Engl J Med 2011 Kopetz et al. J Clin Oncol 2015

36 Level of evidence for actionability

37 Current questions? Should we use large panel of genes? How to develop drugs in rare genomic segments? Prediction of sensitivity? Detecting subclones and monitoring clonal evolution?

38 Genomic predictors of sensitivity Sensitivity to anti-pd1 Mutational burden Number of nonsynonymous mutations Generation of neoantigens Anti-CTLA-4 in Melanoma Anti-PD-1 in NSCLC Snyder et al NEJM 2014 Rizzi et al Science 2015

39 Genomic predictors of sensitivity Pembrolizumab et MSI-High Le et al. Science 2017

40 Sensitivity to targeted agents What is the clinical impact of multiple genomic alterations? Targeted Therapies Each color represent one drug/target couple driver alone Driver + other mutation Wild type (no driver) Co-existing mutations could be associated with resistance to single agent targeted therapies Lefebvre & Yu, Personal data

41 Current questions? Should we use large panel of genes? How to develop drugs in rare genomic segments? Prediction of sensitivity? Detecting subclones and monitoring clonal evolution?

42 CT-scan évolution CA19.9 évolution CGH évolution Detecting subclones and monitoring clonal evolution? Erlotinib 17-Dec-2012 Fin de Traitement 12-Jul-2012 C1J1 Baseline SD (-28%) PR (-36%) PD (New Lesions) 11 Jul Aug Oct Dec 2012 EGFR EGFR MET

43 Perspectives Big data CfDNA

44 Perspectives New generation of software for target prediction & database to train Big Data «no amount of algorithmic finesse can squeeze out information that is not present» (NEJM, 2017) New generation of software Database to train software (Watson ) Trials testing the clinical utility of large panel of genes

45 Integration of multicomponent predictors: example of IO Blank, Science, 2016

46 Perspectives Multigene panels to detect genomic alterations in plasma Performance of NGS on plasma DNA to detect actionable mutation Approach to detect tumor-specific proteins and RNA in blood? Jovelet, Clin Cancer Res, 2015